This paper develops an asynchronous distributed protocol for formation-containment flocking of discrete-time multi-agent systems, addressing moving leaders, cooperative-competitive interactions modeled by signed digraphs, and nonuniform private update schedules within a single design. Using only local neighbor information, each follower is driven to a prescribed barycentric position inside the translating convex hull of the leaders while aligning its velocity with the common leader velocity. The protocol couples absolute-value weighted velocity averaging for flocking with signed relative-position feedback for formation-containment. Convergence is analyzed through the theory of infinite products of sub-stochastic matrices, which yields explicit conditions linking the network topology (positive-path reachability), the control gains, the signed interaction weights, and the inter-update bound, and guarantees geometric convergence. Numerical examples with diamond and pentagram formations confirm the theoretical results and demonstrate scalability.
Starch retrogradation, driven by reassociation and recrystallization of gelatinized amylose and amylopectin chains, induces undesirable firming, syneresis, and sensory deterioration, ultimately shortening the shelf-life of starch-based foods. Enzymatic modification has emerged as a promising route to retard retrogradation, but its efficacy critically depends on the structural features targeted by the enzyme, the susceptibility of the starch substrate, and the accessibility of catalytic sites. This review systematically interprets the structure-dependent mechanisms underlying enzymatic retrogradation regulation, covering the differential regulatory patterns of various enzyme systems. This review further clarifies the essential distinction between substrate structural susceptibility and enzyme catalytic accessibility, and summarizes advanced modification strategies based on porous starch construction and controlled starch swelling to optimize enzyme-substrate interfacial contact efficiency. In addition, mainstream detection techniques are compared based on the different levels of the studied starch structure. Finally, the practical applicability of enzymatic anti-retrogradation technologies in diverse staple and processed starch-based food systems are also summarized. We propose that the key design principle is controlled structural remodeling enabled by enzyme action within a defined processing window, rather than indiscriminate starch degradation. This work provides systematic theoretical insights and practical guidance for the quality stabilization and shelf-life extension of starch-based food products.
White balance (WB) correction for sRGB images captured under multiple illuminations remains challenging due to spatially varying and nonlinear color casts. Existing sRGB-domain WB methods, relying on global adjustment or content-dependent priors, struggle in such scenes because they lack spatial adaptivity correction and explicit illumination guidance. Further, both types of methods lack the consideration of multi-illumination characteristics due to only single-illumination data being available for supervision. To address these limitations, we propose AdaWBNet, an illumination-guided spatially adaptive WB framework formulated under a Maximum A Posteriori estimation paradigm. Within this framework, learnable illumination prototypes serve as priors that capture representative single-illumination patterns, and a K-means cross-attention module enhances these prototypes by assigning local features of multi-illumination inputs to similar prototypes. Then, guided by the illumination prototypes, the likelihood term employs query-adaptive layers to achieve illumination-aware, spatially adaptive color correction. This design enables AdaWBNet to achieve both spatially adaptive color correction and illumination guidance using only single-illumination supervision, thereby allowing it to effectively handle complex multi-illumination scenes. Extensive experiments demonstrate that AdaWBNet outperforms state-of-the-art methods on multi-illumination benchmarks, achieving spatially consistent and perceptually faithful results. The code is available at here.
In this paper, we propose a partially linear additive single-index model. A new loss function is introduced to construct doubly robust and efficient estimation of the regression parameters. The estimators for each nonparametric function are also constructed and their uniform convergence rates are obtained. The asymptotic distributions of all estimators are derived. The bias-corrected empirical likelihood ratios are constructed, and it is shown that each proposed ratio converges in distribution to a standard chi-square variable. The obtained results can be directly used to construct confidence regions of the regression parameters and pointwise confidence intervals for the nonparametric functions. Simulation study and real data analysis demonstrate the robustness and effectiveness of the proposed estimators.
Land use optimization plays pivotal roles in bridging regional sustainable development with national strategic priorities, yet persistent methodological disconnects between diagnostic evaluation and prescriptive optimization hinder practical implementation. Addressing this critical gap, this research develops an integrated “Evaluation-Diagnosis-Simulation-Optimization” (EDSO) framework through synergistic coupling of multi-dimensional functional indices, PLUS modeling, and random forest. The framework utilizes multi-source geospatial data with Lingbao City, Henan Province, China, as the case study area. The results indicate that the transition marked by 1.40% farmland loss due to forest regeneration and urban-rural construction expansion, while non-agricultural output value surged by 23.09% alongside improved infrastructure accessibility (2012–2020). Rural depopulation paralleled the degradation of commercial and public services, exposing vulnerabilities in rural governance. The simulation demonstrates the comprehensive optimized scenario's superiority in landscape efficiency, with 280 million CNY higher ecological value than alternatives. The optimized schedule identified 200.22 km² of land returned to forest and 4.32 km² of fragmented rural low-efficiency built-up land. The schedule identified forest carbon sink peaking at 35-year tree growth cycles, critical insights informing 30m-resolution rotation scheme. The EDSO framework's ability to integrate diagnostic analytics with spatially adaptive interventions offers a transformative toolkit for territorial planning, effectively bridging the science-policy gap.
